SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9676–9700 of 15113 papers

TitleStatusHype
Mission schedule of agile satellites based on Proximal Policy Optimization Algorithm—0
Misspecification in Inverse Reinforcement Learning—0
Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning—0
Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning—0
Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning—0
Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema—0
Mitigating Multi-Stage Cascading Failure by Reinforcement Learning—0
Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers—0
Mitigating Planner Overfitting in Model-Based Reinforcement Learning—0
Mitigating Political Bias in Language Models Through Reinforced Calibration—0
Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization—0
Mitigation of Adversarial Policy Imitation via Constrained Randomization of Policy (CRoP)—0
Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise—0
Mix and Match: Markov Chains & Mixing Times for Matching in Rideshare—0
Mixed Cooperative-Competitive Communication Using Multi-Agent Reinforcement Learning—0
Robust Policy Optimization in Continuous-time Mixed H_2/H_ Stochastic Control—0
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning—0
Mixed-Precision Neural Networks: A Survey—0
Mixed Reinforcement Learning with Additive Stochastic Uncertainty—0
Mixing Human Demonstrations with Self-Exploration in Experience Replay for Deep Reinforcement Learning—0
MIX-MAB: Reinforcement Learning-based Resource Allocation Algorithm for LoRaWAN—0
Mix & Match - Agent Curricula for Reinforcement Learning—0
Mix&Match - Agent Curricula for Reinforcement Learning—0
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees—0
MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified